Add/modify variables (solutions)

↑ Lecture ⇄ Practice

Primary exercises

1. In the survey dataset add a new column feet with heights reported in feet unit (1 foot = 30.48 cm).

mutate(survey, feet=height/30.48)

# A tibble: 233 × 14
   name  gender span1 span2 hand  fold  pulse clap  exerc…¹ smokes height m.i     age
   <chr> <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>   <dbl> <chr> <dbl>
 1 Alys… female  18.5  18   right right    92 left  some    never    173  metr…  18.2
 2 Todd  male    19.5  20.5 left  right   104 left  none    regul    178. impe…  17.6
 3 Gera… male    18    13.3 right left     87 neit… none    occas     NA  <NA>   16.9
 4 Robe… male    18.8  18.9 right right    NA neit… none    never    160  metr…  20.3
 5 Dust… male    20    20   right neit…    35 right some    never    165  metr…  23.7
 6 Abby  female  18    17.7 right left     64 right some    never    173. impe…  21  
 7 Andre male    17.7  17.7 right left     83 right freq    never    183. impe…  18.8
 8 Mich… female  17    17.3 right right    74 right freq    never    157  metr…  35.8
 9 Edwa… male    20    19.5 right right    72 right some    never    175  metr…  19  
10 Carl  male    18.5  18.5 right right    90 right some    never    167  metr…  22.3
# … with 223 more rows, 1 more variable: feet <dbl>, and abbreviated variable name
#   ¹​exercise

2. In the survey dataset add a new column diffWritingHandSpan : the difference of span1 (writing hand) and span2 (non-writing hand).

mutate(survey, diffWritingHandSpan=span1-span2)

# A tibble: 233 × 14
   name  gender span1 span2 hand  fold  pulse clap  exerc…¹ smokes height m.i     age
   <chr> <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>   <dbl> <chr> <dbl>
 1 Alys… female  18.5  18   right right    92 left  some    never    173  metr…  18.2
 2 Todd  male    19.5  20.5 left  right   104 left  none    regul    178. impe…  17.6
 3 Gera… male    18    13.3 right left     87 neit… none    occas     NA  <NA>   16.9
 4 Robe… male    18.8  18.9 right right    NA neit… none    never    160  metr…  20.3
 5 Dust… male    20    20   right neit…    35 right some    never    165  metr…  23.7
 6 Abby  female  18    17.7 right left     64 right some    never    173. impe…  21  
 7 Andre male    17.7  17.7 right left     83 right freq    never    183. impe…  18.8
 8 Mich… female  17    17.3 right right    74 right freq    never    157  metr…  35.8
 9 Edwa… male    20    19.5 right right    72 right some    never    175  metr…  19  
10 Carl  male    18.5  18.5 right right    90 right some    never    167  metr…  22.3
# … with 223 more rows, 1 more variable: diffWritingHandSpan <dbl>, and abbreviated
#   variable name ¹​exercise

3. In the pulse dataset add new weight variables pound and stone (1 kg = 2.20462 pound = 0.157473 stone).

mutate(pulse, pound = weight * 2.20462, stone = weight * 0.157473 )

# A tibble: 110 × 15
   id     name  height weight   age gender smokes alcohol exerc…¹ ran   pulse1 pulse2
   <chr>  <chr>  <dbl>  <dbl> <dbl> <chr>  <chr>  <chr>   <chr>   <chr>  <dbl>  <dbl>
 1 1993_A Bonn…    173     57    18 female no     yes     modera… sat       86     88
 2 1993_B Mela…    179     58    19 female no     yes     modera… ran       82    150
 3 1993_C Cons…    167     62    18 female no     yes     high    ran       96    176
 4 1993_D Trav…    195     84    18 male   no     yes     high    sat       71     73
 5 1993_E Lauri    173     64    18 female no     yes     low     sat       90     88
 6 1993_F Geor…    184     74    22 male   no     yes     low     ran       78    141
 7 1993_G Cher…    162     57    20 female no     yes     modera… sat       68     72
 8 1993_H Fran…    169     55    18 female no     yes     modera… sat       71     77
 9 1993_I Sonja    164     56    19 female no     yes     high    sat       68     68
10 1993_J Troy     168     60    23 male   no     yes     modera… ran       88    150
# … with 100 more rows, 3 more variables: year <dbl>, pound <dbl>, stone <dbl>, and
#   abbreviated variable name ¹​exercise

4. In the survey dataset convert the variables smokes from character to factor with levels {“never”,“occas”,“regul”, “heavy”}, in that order.

mutate(survey, smokes = fct_relevel(factor(smokes), "never","occas","regul", "heavy")) 

# A tibble: 233 × 13
   name  gender span1 span2 hand  fold  pulse clap  exerc…¹ smokes height m.i     age
   <chr> <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>   <fct>   <dbl> <chr> <dbl>
 1 Alys… female  18.5  18   right right    92 left  some    never    173  metr…  18.2
 2 Todd  male    19.5  20.5 left  right   104 left  none    regul    178. impe…  17.6
 3 Gera… male    18    13.3 right left     87 neit… none    occas     NA  <NA>   16.9
 4 Robe… male    18.8  18.9 right right    NA neit… none    never    160  metr…  20.3
 5 Dust… male    20    20   right neit…    35 right some    never    165  metr…  23.7
 6 Abby  female  18    17.7 right left     64 right some    never    173. impe…  21  
 7 Andre male    17.7  17.7 right left     83 right freq    never    183. impe…  18.8
 8 Mich… female  17    17.3 right right    74 right freq    never    157  metr…  35.8
 9 Edwa… male    20    19.5 right right    72 right some    never    175  metr…  19  
10 Carl  male    18.5  18.5 right right    90 right some    never    167  metr…  22.3
# … with 223 more rows, and abbreviated variable name ¹​exercise

Extra exercises

In the survey dataset:

1. Add a new column diffHandSpan : the absolute difference between span1 (writing hand) and span2 (non-writing hand). Hint: use abs function (?abs).

mutate(survey, diffWritingHandSpan=abs(span1-span2))

# A tibble: 233 × 14
   name  gender span1 span2 hand  fold  pulse clap  exerc…¹ smokes height m.i     age
   <chr> <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>   <dbl> <chr> <dbl>
 1 Alys… female  18.5  18   right right    92 left  some    never    173  metr…  18.2
 2 Todd  male    19.5  20.5 left  right   104 left  none    regul    178. impe…  17.6
 3 Gera… male    18    13.3 right left     87 neit… none    occas     NA  <NA>   16.9
 4 Robe… male    18.8  18.9 right right    NA neit… none    never    160  metr…  20.3
 5 Dust… male    20    20   right neit…    35 right some    never    165  metr…  23.7
 6 Abby  female  18    17.7 right left     64 right some    never    173. impe…  21  
 7 Andre male    17.7  17.7 right left     83 right freq    never    183. impe…  18.8
 8 Mich… female  17    17.3 right right    74 right freq    never    157  metr…  35.8
 9 Edwa… male    20    19.5 right right    72 right some    never    175  metr…  19  
10 Carl  male    18.5  18.5 right right    90 right some    never    167  metr…  22.3
# … with 223 more rows, 1 more variable: diffWritingHandSpan <dbl>, and abbreviated
#   variable name ¹​exercise

2. Change height unit from cm to inch (1 cm = 0.393701 inch).

mutate(survey, height=height*0.393701)

# A tibble: 233 × 13
   name  gender span1 span2 hand  fold  pulse clap  exerc…¹ smokes height m.i     age
   <chr> <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>   <dbl> <chr> <dbl>
 1 Alys… female  18.5  18   right right    92 left  some    never    68.1 metr…  18.2
 2 Todd  male    19.5  20.5 left  right   104 left  none    regul    70.0 impe…  17.6
 3 Gera… male    18    13.3 right left     87 neit… none    occas    NA   <NA>   16.9
 4 Robe… male    18.8  18.9 right right    NA neit… none    never    63.0 metr…  20.3
 5 Dust… male    20    20   right neit…    35 right some    never    65.0 metr…  23.7
 6 Abby  female  18    17.7 right left     64 right some    never    68.0 impe…  21  
 7 Andre male    17.7  17.7 right left     83 right freq    never    72.0 impe…  18.8
 8 Mich… female  17    17.3 right right    74 right freq    never    61.8 metr…  35.8
 9 Edwa… male    20    19.5 right right    72 right some    never    68.9 metr…  19  
10 Carl  male    18.5  18.5 right right    90 right some    never    65.7 metr…  22.3
# … with 223 more rows, and abbreviated variable name ¹​exercise

3. Produce the tibble containing the personal information of only those having height (in foot unit) between and including 6.0 and 6.5 feet.

filter( 
  mutate( 
    select(survey, Name=name, Age=age, Gender=gender, Height=height), feet=Height/30.48 
  ), feet >= 6.0 & feet <= 6.5 
) 

# A tibble: 36 × 5
   Name        Age Gender Height  feet
   <chr>     <dbl> <chr>   <dbl> <dbl>
 1 Andre      18.8 male     183.  6   
 2 Virgil     18.3 male     183.  6   
 3 Ken        19.8 male     190.  6.25
 4 Virgil     17.9 male     190.  6.25
 5 Frank      18.2 male     184   6.04
 6 Nathaniel  18   male     190   6.23
 7 Ben        35.5 male     185   6.07
 8 Bernard    17.5 male     190   6.23
 9 Felix      20.3 male     187   6.14
10 Patrick    18   male     183   6.00
# … with 26 more rows

4. How many observations are in the following call, please try to reason before running the statement.

filter(mutate(filter(survey, height> 190),feet=height/30.48),height<=190 )

5. The following table is one of many BMI classifications:

	classification	bmi
1	underweight	<18.5
2	normal	18.5-24.9
3	overweight	25-29.9
4	obese	>=30

Add the variable BMI_class to the pulse data set. Note that you will need to round (see ?round an additional argument digits) the BMI calculation to one decimal digit to fit into classification values in the table given above. Use the helper function case_when.

pulse_bmi <- mutate(pulse, BMI=round( weight/((height/100)^2), digits = 1)  ) 
mutate(pulse_bmi, BMI_class = case_when(
                BMI < 18.5 ~ "underweight",
                BMI >= 18.5 & BMI <= 24.9 ~ "normal",
                BMI >= 25   & BMI <= 29.9 ~ "overweight",
                BMI >= 30 ~ "obese"
              )                     
      )

# A tibble: 110 × 15
   id     name  height weight   age gender smokes alcohol exerc…¹ ran   pulse1 pulse2
   <chr>  <chr>  <dbl>  <dbl> <dbl> <chr>  <chr>  <chr>   <chr>   <chr>  <dbl>  <dbl>
 1 1993_A Bonn…    173     57    18 female no     yes     modera… sat       86     88
 2 1993_B Mela…    179     58    19 female no     yes     modera… ran       82    150
 3 1993_C Cons…    167     62    18 female no     yes     high    ran       96    176
 4 1993_D Trav…    195     84    18 male   no     yes     high    sat       71     73
 5 1993_E Lauri    173     64    18 female no     yes     low     sat       90     88
 6 1993_F Geor…    184     74    22 male   no     yes     low     ran       78    141
 7 1993_G Cher…    162     57    20 female no     yes     modera… sat       68     72
 8 1993_H Fran…    169     55    18 female no     yes     modera… sat       71     77
 9 1993_I Sonja    164     56    19 female no     yes     high    sat       68     68
10 1993_J Troy     168     60    23 male   no     yes     modera… ran       88    150
# … with 100 more rows, 3 more variables: year <dbl>, BMI <dbl>, BMI_class <chr>,
#   and abbreviated variable name ¹​exercise

6. Age classification:

	classification	age	notation
1	adult	>19	(19,∞)
2	adolescent	>=10 and <=19	[10,19]
3	child	>=1 and <=9	[1,9]
4	infant	<1	(-∞,1)

Add the variable age_group to the survey dataset.

mutate(survey, age_group = case_when(
                age > 19 ~ "adult",
                age >= 10 & age <= 19 ~ "adolescent",
                age > 1   & age <= 9 ~ "child",
                age <= 1 ~ "infant"
              )                     
      )

# A tibble: 233 × 14
   name  gender span1 span2 hand  fold  pulse clap  exerc…¹ smokes height m.i     age
   <chr> <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>   <chr>   <dbl> <chr> <dbl>
 1 Alys… female  18.5  18   right right    92 left  some    never    173  metr…  18.2
 2 Todd  male    19.5  20.5 left  right   104 left  none    regul    178. impe…  17.6
 3 Gera… male    18    13.3 right left     87 neit… none    occas     NA  <NA>   16.9
 4 Robe… male    18.8  18.9 right right    NA neit… none    never    160  metr…  20.3
 5 Dust… male    20    20   right neit…    35 right some    never    165  metr…  23.7
 6 Abby  female  18    17.7 right left     64 right some    never    173. impe…  21  
 7 Andre male    17.7  17.7 right left     83 right freq    never    183. impe…  18.8
 8 Mich… female  17    17.3 right right    74 right freq    never    157  metr…  35.8
 9 Edwa… male    20    19.5 right right    72 right some    never    175  metr…  19  
10 Carl  male    18.5  18.5 right right    90 right some    never    167  metr…  22.3
# … with 223 more rows, 1 more variable: age_group <chr>, and abbreviated variable
#   name ¹​exercise

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